论文标题
旋律分类器与堆叠lstm
Melody Classifier with Stacked-LSTM
论文作者
论文摘要
近年来，尝试将生成模型用于音乐发电一直很普遍，其中一些已经取得了良好的结果。其中一些模型产生的作品几乎与由人类作曲家组成的作品没有区别。但是，关于机器生成音乐的评估系统的研究仍处于相对较早的阶段，并且对于此类任务没有统一的标准。本文提出了一个基于语言模型的堆叠的LSTM二进制分类器，该分类器可用于通过学习MIDI文件的音调，位置和持续时间来区分人类作曲家的作品与机器生成的旋律。
Attempts to use generative models for music generation have been common in recent years, and some of them have achieved good results. Pieces generated by some of these models are almost indistinguishable from those being composed by human composers. However, the research on the evaluation system for machine-generated music is still at a relatively early stage, and there is no uniform standard for such tasks. This paper proposes a stacked-LSTM binary classifier based on a language model, which can be used to distinguish the human composer's work from the machine-generated melody by learning the MIDI file's pitch, position, and duration.
